[1]} use a combination of two approaches in their study of sarcasm. They propose a parsing algorithm that looks for sentiment-bearing situations and identifies sarcasm in forms of a contradiction of negative (or positive) sentiment and positive (or negative) situation. They also look for the co-occurrence of interjection hyperbolic words like “wow”, “yay”, etc. at the start of tweets, and intensifiers like “absolutely”, “huge” e.g., “Wow, that's a huge discount, I'm not buying anything!! #sarcasm.” Similarly, [2]} find a positive/negative contrast between a sentiment and a situation helpful, and indicative of sarcasm, e.g., “I’m so pleased mom woke me up with vacuuming my room this morning. :)”. Likewise, [3]} speculate that sentiment incongruity within an utterance signifies sarcasm. To this end, they gather all real-world concepts that carry an implicit sentiment and label them with either a “positive” or “negative” sentiment label. For example, “going to the dentist” is often associated with a negative sentiment. Although their model does not surpass the baseline, they highlight the difficulty and importance of incorporating sarcasm detection into sentiment classifiers. They view their efforts as an extension of the seminal work by [4]} to use a concept called syntactic packaging to demonstrate the influence of syntactic choices on the perceived implicit sentiment of news headlines.
